Considering　the　fact　that　driving　behavior　data　possesses　characteristics　of　strong　real-time,　poor　stability,　and　continuous　change,　this　study　proposes　the　Individual　Driving　Behavior　Graph　Construction　Method　(DBGCM),　which　visually　presents　the　time　trajectory　of　driving　behavior　to　explore　safety-ecological　(SAF-ECO)　characteristics　of　individual　drivers.　The　results　can　be　applied　in　the　analysis　of　driving　safety　ecology　and　as　a　reference　for　driving　behavior　optimization.　This　study　is　based　on　the　micro-driving　behavior　data　collected　by　the　on-board　diagnostic　devices　(OBD),　which　can　create　a　graph　on　individual　driver　behavior　characteristics　via　nodes　and　time　axis　as　its　elements.　Additionally,　the　method　of　Longest　Common　Subsequence　(LCSS)　is　proposed　to　identify　the　similarity　among　different　driving　behavior　graphs.　The　data　results　of　taxi　drivers　under　different　SAF-ECO　levels　lead　to　the　conclusion　that　the　driving　behavior　characteristics　graph　analysis　is　consistent　with　the　SAF-ECO　classification.　The　similarity　of　graphs　among　＂safe　and　non-eco＂　drivers　is　higher　than　that　within　other　categories.　Finally,　the　research　discusses　in　detail　the　data　requirements,　method　verification,　and　future　applications.　The　reasonable　coupling　characteristic　description　of　＂SAF-ECO＂　driving　behavior　is　conducive　to　the　enhancement　of　drivers＇　self-management　ability,　driving　education,　and　customization　for　drivers.